Instructions to use DLNorb/ta_s3c10_dpoc03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DLNorb/ta_s3c10_dpoc03 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DLNorb/ta_s3c10_dpoc03")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DLNorb/ta_s3c10_dpoc03")
model = AutoModelForCausalLM.from_pretrained("DLNorb/ta_s3c10_dpoc03")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DLNorb/ta_s3c10_dpoc03 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DLNorb/ta_s3c10_dpoc03"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DLNorb/ta_s3c10_dpoc03",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DLNorb/ta_s3c10_dpoc03

SGLang

How to use DLNorb/ta_s3c10_dpoc03 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "DLNorb/ta_s3c10_dpoc03" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DLNorb/ta_s3c10_dpoc03",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "DLNorb/ta_s3c10_dpoc03" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DLNorb/ta_s3c10_dpoc03",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DLNorb/ta_s3c10_dpoc03 with Docker Model Runner:
```
docker model run hf.co/DLNorb/ta_s3c10_dpoc03
```

ta_s3c10_dpoc03

This is a fully merged model based on Qwen/Qwen3-4B-Instruct-2507, optimized for structured output generation (JSON / YAML / XML / TOML / CSV).

Merge Strategy

Task Arithmetic merge of SFT Stage 3 and DPO deltas on top of the base model:

merged = base + 1.0 * (sft_s3 - base) + 0.3 * (dpo - base)

Source Adapters

SFT Stage 3: DLNorb/lora_structeval_t_qwen3_4b_v2_stage3 (checkpoint-100)
- QLoRA r=32, alpha=64, trained on u-10bei/structured_data_with_cot_dataset_512_v5
- CoT-masked SFT: loss applied only to final structured output
DPO: DLNorb/dpo_lora_model_stage3 (checkpoint-505)
- QLoRA r=8, alpha=16, trained on u-10bei/dpo-dataset-qwen-cot
- Applied on top of SFT Stage 3

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "DLNorb/ta_s3c10_dpoc03"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [{"role": "user", "content": "Convert this to JSON: name=Alice, age=30"}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=2048, do_sample=False)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Sources & Terms (IMPORTANT)

Training data:

https://huggingface.co/datasets/u-10bei/structured_data_with_cot_dataset_512_v5: MIT License
https://huggingface.co/datasets/u-10bei/dpo-dataset-qwen-cot: MIT License

Compliance: Users must comply with each dataset's license (including copyright notice) and the base model's original terms of use.

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